ChordAIS: An assistive system for the generation of chord progressions with an artificial immune system

Abstract Chord progressions play an important role in Western tonal music. For a novice composer, the creation of chord progressions can be challenging because it involves many subjective factors, such as the musical context, personal preference and aesthetic choices. This work proposes ChordAIS, an interactive system that assists the user in generating chord progressions by iteratively adding new chords. At each iteration a search for the next candidate chord is performed in the Tonal Interval Space (TIS), where distances capture perceptual features of pitch configurations on different levels, such as musical notes, chords, and scales. We use an artificial immune system (AIS) called opt-aiNet to search for candidate chords by optimizing an objective function that encodes desirable musical properties of chord progressions as distances in the TIS. Opt-aiNet is capable of finding multiple optima of multi-modal functions simultaneously, resulting in multiple good-quality candidate chords which can be added to the progression by the user. To validate ChordAIS, we performed different experiments and a listening test to evaluate the perceptual quality of the candidate chords proposed by ChordAIS. Most listeners rated the chords proposed by ChordAIS as better candidates for progressions than the chords discarded by ChordAIS. Then, we compared ChordAIS with two similar systems, ConChord and ChordGA, which uses a standard GA instead of opt-aiNet. A user test showed that ChordAIS was preferred over ChordGA and Conchord. According to the results, ChordAIS was deemed capable of assisting the users in the generation of tonal chord progressions by proposing good-quality candidates in all the keys tested.

[1]  Jonathan P. J. Stock,et al.  The Application of Schenkerian Analysis to Ethnomusicology: Problems and Possibilities , 1993 .

[2]  Richard Cohn Neo-Riemannian Operations, Parsimonious Trichords, and Their "Tonnetz" Representations , 1997 .

[3]  Kenneth Sörensen,et al.  Generating structured music for bagana using quality metrics based on Markov models , 2015, Expert Syst. Appl..

[4]  Gilberto Bernardes,et al.  A multi-level tonal interval space for modelling pitch relatedness and musical consonance , 2016 .

[5]  Geraint A. Wiggins,et al.  Motivations and Methodologies for Automation of the Compositional Process , 2002 .

[6]  Gilberto Bernardes,et al.  Harmony Generation Driven by a Perceptually Motivated Tonal Interval Space , 2016, CIE.

[7]  Geraint A. Wiggins,et al.  Evaluating Cognitive Models of Musical Composition , 2007 .

[8]  Maha Abdelhaq,et al.  Securing Mobile Ad Hoc Networks Using Danger Theory-Based Artificial Immune Algorithm , 2015, PloS one.

[9]  Kenneth Sörensen,et al.  Composing fifth species counterpoint music with a variable neighborhood search algorithm , 2013, Expert Syst. Appl..

[10]  Ponnuthurai N. Suganthan,et al.  Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art , 2011, Swarm Evol. Comput..

[11]  L.N. de Castro,et al.  An artificial immune network for multimodal function optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[12]  Robin C. Laney,et al.  Developing and evaluating computational models of musical style , 2015, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[13]  Yiannis Kompatsiaris,et al.  Large-scale evaluation of splicing localization algorithms for web images , 2017, Multimedia Tools and Applications.

[14]  Frederico G. Guimarães,et al.  Overview of Artificial Immune Systems for Multi-objective Optimization , 2007, EMO.

[15]  Joel Lester,et al.  Rameau and eighteenth-century harmonic theory , 2002 .

[16]  Mark L. Berenson,et al.  Basic Business Statistics : Concepts and Applications , 2007 .

[17]  Georg Groh,et al.  A Genetic Algorithm Approach to Collaborative Music Creation on a Multi-Touch Table , 2014, ICMC.

[18]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[19]  Carlos A. Coello Coello,et al.  Solving Multiobjective Optimization Problems Using an Artificial Immune System , 2005, Genetic Programming and Evolvable Machines.

[20]  Morten Meilgaard,et al.  Sensory Evaluation Techniques , 2020 .

[21]  C. Harte,et al.  Detecting harmonic change in musical audio , 2006, AMCMM '06.

[22]  Guillermo Hough,et al.  Number of consumers necessary for sensory acceptability tests , 2006 .

[23]  Artemis Moroni,et al.  Vox Populi: An Interactive Evolutionary System for Algorithmic Music Composition , 2000, Leonardo Music Journal.

[24]  Penousal Machado,et al.  A Corpus-Based Hybrid Approach to Music Analysis and Composition , 2007, AAAI.

[25]  Jianfa Wu,et al.  Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm , 2015, PloS one.

[26]  David Cope,et al.  Computer Modeling of Musical Intelligence in EMI , 1992 .

[27]  Miguel Molina-Solana,et al.  Inmamusys: Intelligent multiagent music system , 2009, Expert Syst. Appl..

[28]  Simon Lui A Real Time Common Chord Progression Guide on the Smartphone for Jamming Pop Song on the Music Keyboard , 2014, NIME.

[29]  François Pachet,et al.  The Continuator: Musical Interaction With Style , 2003, ICMC.

[30]  Jean-François Paiement,et al.  A Probabilistic Model for Chord Progressions , 2005, ISMIR.

[31]  Fred Lerdahl,et al.  Tonal Pitch Space , 2001 .

[32]  Makoto Fukumoto,et al.  Creation of Music Chord Progression Suited for User's Feelings Based on Interactive Genetic Algorithm , 2014, 2014 IIAI 3rd International Conference on Advanced Applied Informatics.

[33]  Haider Banka,et al.  An Automatic Chord Progression Generator Based On Reinforcement Learning , 2018, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[34]  Mark Steedman,et al.  A Generative Grammar for Jazz Chord Sequences , 1984 .

[35]  Yuko Osana,et al.  Automatic melody generation considering chord progression by genetic algorithm , 2014, 2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014).

[36]  Katsutoshi Itoyama,et al.  A music performance assistance system based on vocal, harmonic, and percussive source separation and content visualization for music audio signals , 2015 .

[37]  Arne Eigenfeldt,et al.  Realtime Generation of Harmonic Progressions Using Constrained Markov Selection , 2010, ICCC.

[38]  Eytan Agmon,et al.  Functional Harmony Revisited: A Prototype-Theoretic Approach , 1995 .

[39]  Shingchern D. You,et al.  Automatic chord generation system using basic music theory and genetic algorithm , 2016, 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

[40]  Kemal Ebcioglu,et al.  An Expert System for Harmonizing Chorales in the Style of J. S. Bach , 1990, J. Log. Program..

[41]  Michael N. Vrahatis,et al.  Interactive music composition driven by feature evolution , 2016, SpringerPlus.

[42]  Juan M. Corchado,et al.  Automatic Generation of Chord Progressions with an Artificial Immune System , 2015, EvoMUSART.

[43]  Omar López-Ortega,et al.  Fractals, fuzzy logic and expert systems to assist in the construction of musical pieces , 2012, Expert Syst. Appl..

[44]  Patrick C. K. Hung,et al.  Collaborative service system design for music content creation , 2014, Inf. Syst. Frontiers.

[45]  Patricia Carpenter,et al.  The Musical Idea and the Logic, Technique, and Art of Its Presentation , 1995 .